pygrinder
A Python toolkit for introducing missing values into datasets
Decision gist · record as of 2026-08-14
Yes. PyGrinder is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a specific and common problem in machine learning: creating incomplete datasets for model evaluation. The low install friction and production-stable status make it a straightforward choice if you need to simulate missing data patterns. Install it if you work with time-series data or incomplete observations in model development or research.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8; torch dependency may require separate installation depending on your system.
- Low friction installation via wheel; active maintenance with recent commits and production-stable status.
- Runtime dependencies on numpy, scikit-learn, pandas, torch, and tsdb are standard data science libraries.
License · maintenance · safety
permissive license (permissive) — BSD license (permissive); allows commercial and private use with attribution and liability disclaimers. No restrictions on modification or redistribution.
last release 2025-02-03 (557 days) · last repo commit 2026-08-12 · 69 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 122,792 downloads/mo, #11,933 on PyPI
Alternatives
Verify before relying
pip install pygrinder
import numpy as np
from pygrinder import mcar, calc_missing_rate
ts_dataset = np.random.randn(128, 10, 36)
X_with_mcar = mcar(ts_dataset, p=0.1)
missing_rate = calc_missing_rate(X_with_mcar)- Whether all five runtime dependencies (torch, tsdb, etc.) are always required or only for specific patterns.
- Performance characteristics when working with very large time-series datasets.
- Whether the package supports GPU acceleration through torch.
What it is and what it does
PyGrinder is a data corruption toolkit designed to inject missing values into datasets using multiple well-defined patterns. It was originally part of PyPOTS (a time-series data mining framework) and separated to decouple missingness generation from learning algorithms. The package supports MCAR (Missing Completely At Random), MAR (Missing At Random), MNAR (Missing Not At Random with variants), RDO (Random Data Observation), and structured patterns like sequential and block missing values.
The typical use case is preparing synthetic incomplete datasets for evaluating how machine learning models handle partial observations or for testing data reconstruction algorithms. You pass a numpy array (often time-series data with shape like samples × timesteps × features) and a pattern function with parameters controlling the missing rate or structure, and receive back a dataset with missing values injected. The package also provides a utility to calculate the resulting missing rate.
Use it for
- Evaluate time-series imputation models by creating datasets with known missingness patterns to test reconstruction accuracy.
- Generate partially-observed datasets for training robust models that must handle incomplete real-world observations.
- Benchmark data mining algorithms on incomplete data without needing to manually corrupt or collect partial datasets.
- Simulate different missing-data mechanisms (MCAR vs. MNAR) to study how model performance varies with missingness type.
- Create test suites for data preprocessing pipelines that must handle missing values in production.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyGrinder is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a specific and common problem in machine learning: creating incomplete datasets for model evaluation. The low install friction and production-stable status make it a straightforward choice if you need to simulate missing data patterns. Install it if you work with time-series data or incomplete observations in model development or research.
Install
pygrinder on PyPI
Before you install
Low friction installation via wheel; active maintenance with recent commits and production-stable status. Runtime dependencies on numpy, scikit-learn, pandas, torch, and tsdb are standard data science libraries.
Requires Python >=3.8; torch dependency may require separate installation depending on your system.
License in practice
BSD license (permissive); allows commercial and private use with attribution and liability disclaimers. No restrictions on modification or redistribution.
Quickstart
pip install pygrinder
import numpy as np
from pygrinder import mcar, calc_missing_rate
ts_dataset = np.random.randn(128, 10, 36)
X_with_mcar = mcar(ts_dataset, p=0.1)
missing_rate = calc_missing_rate(X_with_mcar)Verify before relying
- Whether all five runtime dependencies (torch, tsdb, etc.) are always required or only for specific patterns.
- Performance characteristics when working with very large time-series datasets.
- Whether the package supports GPU acceleration through torch.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyscikit-learnpandastorchtsdb |
| Maintenance | Actively maintained 557 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 122,792 / month, #11,933 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Application Frameworks |
Evidence: pygrinder-0.7-py3-none-any.whl
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